Overview

Dataset statistics

Number of variables25
Number of observations23921
Missing cells126580
Missing cells (%)21.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory193.0 B

Variable types

Categorical9
Numeric14
Boolean2

Alerts

date has a high cardinality: 5550 distinct values High cardinality
home_team has a high cardinality: 211 distinct values High cardinality
away_team has a high cardinality: 211 distinct values High cardinality
tournament has a high cardinality: 82 distinct values High cardinality
city has a high cardinality: 1576 distinct values High cardinality
country has a high cardinality: 217 distinct values High cardinality
home_team_fifa_rank is highly correlated with away_team_fifa_rank and 6 other fieldsHigh correlation
away_team_fifa_rank is highly correlated with home_team_fifa_rank and 6 other fieldsHigh correlation
home_team_total_fifa_points is highly correlated with home_team_fifa_rank and 5 other fieldsHigh correlation
away_team_total_fifa_points is highly correlated with away_team_fifa_rank and 6 other fieldsHigh correlation
home_team_goalkeeper_score is highly correlated with home_team_fifa_rank and 4 other fieldsHigh correlation
away_team_goalkeeper_score is highly correlated with away_team_fifa_rank and 4 other fieldsHigh correlation
home_team_mean_defense_score is highly correlated with home_team_fifa_rank and 4 other fieldsHigh correlation
home_team_mean_offense_score is highly correlated with home_team_fifa_rank and 4 other fieldsHigh correlation
home_team_mean_midfield_score is highly correlated with home_team_fifa_rank and 5 other fieldsHigh correlation
away_team_mean_defense_score is highly correlated with away_team_fifa_rank and 4 other fieldsHigh correlation
away_team_mean_offense_score is highly correlated with away_team_fifa_rank and 4 other fieldsHigh correlation
away_team_mean_midfield_score is highly correlated with away_team_fifa_rank and 4 other fieldsHigh correlation
away_team_continent is highly correlated with home_team_continent and 1 other fieldsHigh correlation
tournament is highly correlated with home_team_continent and 6 other fieldsHigh correlation
home_team_continent is highly correlated with away_team_continent and 1 other fieldsHigh correlation
neutral_location is highly correlated with tournamentHigh correlation
away_team_score is highly correlated with home_team_resultHigh correlation
home_team_result is highly correlated with away_team_scoreHigh correlation
home_team_goalkeeper_score has 15542 (65.0%) missing values Missing
away_team_goalkeeper_score has 15826 (66.2%) missing values Missing
home_team_mean_defense_score has 16134 (67.4%) missing values Missing
home_team_mean_offense_score has 15411 (64.4%) missing values Missing
home_team_mean_midfield_score has 15759 (65.9%) missing values Missing
away_team_mean_defense_score has 16357 (68.4%) missing values Missing
away_team_mean_offense_score has 15609 (65.3%) missing values Missing
away_team_mean_midfield_score has 15942 (66.6%) missing values Missing
home_team_total_fifa_points has 14290 (59.7%) zeros Zeros
away_team_total_fifa_points has 14288 (59.7%) zeros Zeros
home_team_score has 6273 (26.2%) zeros Zeros
away_team_score has 9558 (40.0%) zeros Zeros

Reproduction

Analysis started2022-10-09 15:05:18.916817
Analysis finished2022-10-09 15:05:50.122228
Duration31.21 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY

Distinct5550
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
2012-02-29
 
66
2016-03-29
 
59
2008-03-26
 
59
2014-03-05
 
57
2022-03-29
 
55
Other values (5545)
23625 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters239210
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1934 ?
Unique (%)8.1%

Sample

1st row1993-08-08
2nd row1993-08-08
3rd row1993-08-08
4th row1993-08-08
5th row1993-08-08

Common Values

ValueCountFrequency (%)
2012-02-2966
 
0.3%
2016-03-2959
 
0.2%
2008-03-2659
 
0.2%
2014-03-0557
 
0.2%
2022-03-2955
 
0.2%
2012-11-1455
 
0.2%
2011-10-1154
 
0.2%
2011-11-1154
 
0.2%
2011-11-1553
 
0.2%
2011-09-0252
 
0.2%
Other values (5540)23357
97.6%

Length

2022-10-09T12:05:50.177092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-02-2966
 
0.3%
2008-03-2659
 
0.2%
2016-03-2959
 
0.2%
2014-03-0557
 
0.2%
2012-11-1455
 
0.2%
2022-03-2955
 
0.2%
2011-10-1154
 
0.2%
2011-11-1154
 
0.2%
2011-11-1553
 
0.2%
2011-09-0252
 
0.2%
Other values (5540)23357
97.6%

Most occurring characters

ValueCountFrequency (%)
060867
25.4%
-47842
20.0%
138674
16.2%
235093
14.7%
915929
 
6.7%
69098
 
3.8%
37542
 
3.2%
76383
 
2.7%
86307
 
2.6%
55981
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number191368
80.0%
Dash Punctuation47842
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
060867
31.8%
138674
20.2%
235093
18.3%
915929
 
8.3%
69098
 
4.8%
37542
 
3.9%
76383
 
3.3%
86307
 
3.3%
55981
 
3.1%
45494
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
-47842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common239210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
060867
25.4%
-47842
20.0%
138674
16.2%
235093
14.7%
915929
 
6.7%
69098
 
3.8%
37542
 
3.2%
76383
 
2.7%
86307
 
2.6%
55981
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII239210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
060867
25.4%
-47842
20.0%
138674
16.2%
235093
14.7%
915929
 
6.7%
69098
 
3.8%
37542
 
3.2%
76383
 
2.7%
86307
 
2.6%
55981
 
2.5%

home_team
Categorical

HIGH CARDINALITY

Distinct211
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Mexico
 
316
USA
 
314
Japan
 
280
Saudi Arabia
 
272
Korea Republic
 
249
Other values (206)
22490 

Length

Max length30
Median length22
Mean length8.101793403
Min length3

Characters and Unicode

Total characters193803
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBolivia
2nd rowBrazil
3rd rowEcuador
4th rowGuinea
5th rowParaguay

Common Values

ValueCountFrequency (%)
Mexico316
 
1.3%
USA314
 
1.3%
Japan280
 
1.2%
Saudi Arabia272
 
1.1%
Korea Republic249
 
1.0%
Qatar249
 
1.0%
Oman241
 
1.0%
United Arab Emirates239
 
1.0%
Brazil233
 
1.0%
South Africa229
 
1.0%
Other values (201)21299
89.0%

Length

2022-10-09T12:05:50.273612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic714
 
2.4%
and500
 
1.7%
korea323
 
1.1%
mexico316
 
1.1%
usa314
 
1.1%
ireland291
 
1.0%
japan280
 
0.9%
arabia272
 
0.9%
saudi272
 
0.9%
islands267
 
0.9%
Other values (236)25994
88.0%

Most occurring characters

ValueCountFrequency (%)
a29866
15.4%
i15905
 
8.2%
n14851
 
7.7%
e12723
 
6.6%
r11585
 
6.0%
o10169
 
5.2%
l7550
 
3.9%
u6692
 
3.5%
t6654
 
3.4%
d6305
 
3.3%
Other values (50)71503
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter157625
81.3%
Uppercase Letter30186
 
15.6%
Space Separator5622
 
2.9%
Other Punctuation313
 
0.2%
Dash Punctuation57
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a29866
18.9%
i15905
10.1%
n14851
9.4%
e12723
 
8.1%
r11585
 
7.3%
o10169
 
6.5%
l7550
 
4.8%
u6692
 
4.2%
t6654
 
4.2%
d6305
 
4.0%
Other values (21)35325
22.4%
Uppercase Letter
ValueCountFrequency (%)
S3193
 
10.6%
A2539
 
8.4%
C2249
 
7.5%
M2076
 
6.9%
B2007
 
6.6%
I1977
 
6.5%
R1940
 
6.4%
E1466
 
4.9%
T1430
 
4.7%
G1420
 
4.7%
Other values (15)9889
32.8%
Other Punctuation
ValueCountFrequency (%)
.159
50.8%
'154
49.2%
Space Separator
ValueCountFrequency (%)
5622
100.0%
Dash Punctuation
ValueCountFrequency (%)
-57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin187811
96.9%
Common5992
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a29866
15.9%
i15905
 
8.5%
n14851
 
7.9%
e12723
 
6.8%
r11585
 
6.2%
o10169
 
5.4%
l7550
 
4.0%
u6692
 
3.6%
t6654
 
3.5%
d6305
 
3.4%
Other values (46)65511
34.9%
Common
ValueCountFrequency (%)
5622
93.8%
.159
 
2.7%
'154
 
2.6%
-57
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII193565
99.9%
None238
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a29866
15.4%
i15905
 
8.2%
n14851
 
7.7%
e12723
 
6.6%
r11585
 
6.0%
o10169
 
5.3%
l7550
 
3.9%
u6692
 
3.5%
t6654
 
3.4%
d6305
 
3.3%
Other values (45)71265
36.8%
None
ValueCountFrequency (%)
ô154
64.7%
ç33
 
13.9%
ã17
 
7.1%
é17
 
7.1%
í17
 
7.1%

away_team
Categorical

HIGH CARDINALITY

Distinct211
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Zambia
 
243
Costa Rica
 
217
Paraguay
 
216
Sweden
 
206
Mexico
 
201
Other values (206)
22838 

Length

Max length30
Median length22
Mean length8.126959575
Min length3

Characters and Unicode

Total characters194405
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUruguay
2nd rowMexico
3rd rowVenezuela
4th rowSierra Leone
5th rowArgentina

Common Values

ValueCountFrequency (%)
Zambia243
 
1.0%
Costa Rica217
 
0.9%
Paraguay216
 
0.9%
Sweden206
 
0.9%
Mexico201
 
0.8%
Brazil200
 
0.8%
Jamaica199
 
0.8%
Saudi Arabia199
 
0.8%
Iraq199
 
0.8%
Ghana198
 
0.8%
Other values (201)21843
91.3%

Length

2022-10-09T12:05:50.377366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic641
 
2.2%
and511
 
1.7%
korea331
 
1.1%
islands284
 
1.0%
ireland252
 
0.9%
zambia243
 
0.8%
guinea241
 
0.8%
congo229
 
0.8%
costa217
 
0.7%
rica217
 
0.7%
Other values (236)26234
89.2%

Most occurring characters

ValueCountFrequency (%)
a30129
15.5%
i16256
 
8.4%
n14765
 
7.6%
e13309
 
6.8%
r11382
 
5.9%
o10235
 
5.3%
l7532
 
3.9%
u7045
 
3.6%
t6392
 
3.3%
d6135
 
3.2%
Other values (50)71225
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter158602
81.6%
Uppercase Letter29877
 
15.4%
Space Separator5479
 
2.8%
Other Punctuation354
 
0.2%
Dash Punctuation93
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a30129
19.0%
i16256
10.2%
n14765
9.3%
e13309
 
8.4%
r11382
 
7.2%
o10235
 
6.5%
l7532
 
4.7%
u7045
 
4.4%
t6392
 
4.0%
d6135
 
3.9%
Other values (21)35422
22.3%
Uppercase Letter
ValueCountFrequency (%)
S3129
 
10.5%
C2507
 
8.4%
A2064
 
6.9%
B2041
 
6.8%
M1979
 
6.6%
I1902
 
6.4%
R1881
 
6.3%
P1536
 
5.1%
G1534
 
5.1%
T1449
 
4.8%
Other values (15)9855
33.0%
Other Punctuation
ValueCountFrequency (%)
'182
51.4%
.172
48.6%
Space Separator
ValueCountFrequency (%)
5479
100.0%
Dash Punctuation
ValueCountFrequency (%)
-93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin188479
97.0%
Common5926
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a30129
16.0%
i16256
 
8.6%
n14765
 
7.8%
e13309
 
7.1%
r11382
 
6.0%
o10235
 
5.4%
l7532
 
4.0%
u7045
 
3.7%
t6392
 
3.4%
d6135
 
3.3%
Other values (46)65299
34.6%
Common
ValueCountFrequency (%)
5479
92.5%
'182
 
3.1%
.172
 
2.9%
-93
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII194113
99.8%
None292
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a30129
15.5%
i16256
 
8.4%
n14765
 
7.6%
e13309
 
6.9%
r11382
 
5.9%
o10235
 
5.3%
l7532
 
3.9%
u7045
 
3.6%
t6392
 
3.3%
d6135
 
3.2%
Other values (45)70933
36.5%
None
ValueCountFrequency (%)
ô182
62.3%
ç35
 
12.0%
ã25
 
8.6%
é25
 
8.6%
í25
 
8.6%

home_team_continent
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Europe
7593 
Africa
5885 
Asia
5302 
North America
2772 
South America
1839 

Length

Max length13
Median length6
Mean length6.92818026
Min length4

Characters and Unicode

Total characters165729
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth America
2nd rowSouth America
3rd rowSouth America
4th rowAfrica
5th rowSouth America

Common Values

ValueCountFrequency (%)
Europe7593
31.7%
Africa5885
24.6%
Asia5302
22.2%
North America2772
 
11.6%
South America1839
 
7.7%
Oceania530
 
2.2%

Length

2022-10-09T12:05:50.473513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-09T12:05:50.579829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
europe7593
26.6%
africa5885
20.6%
asia5302
18.6%
america4611
16.2%
north2772
 
9.7%
south1839
 
6.4%
oceania530
 
1.9%

Most occurring characters

ValueCountFrequency (%)
r20861
12.6%
a16858
10.2%
i16328
9.9%
A15798
9.5%
e12734
 
7.7%
o12204
 
7.4%
c11026
 
6.7%
u9432
 
5.7%
E7593
 
4.6%
p7593
 
4.6%
Other values (10)35302
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter132586
80.0%
Uppercase Letter28532
 
17.2%
Space Separator4611
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r20861
15.7%
a16858
12.7%
i16328
12.3%
e12734
9.6%
o12204
9.2%
c11026
8.3%
u9432
7.1%
p7593
 
5.7%
f5885
 
4.4%
s5302
 
4.0%
Other values (4)14363
10.8%
Uppercase Letter
ValueCountFrequency (%)
A15798
55.4%
E7593
26.6%
N2772
 
9.7%
S1839
 
6.4%
O530
 
1.9%
Space Separator
ValueCountFrequency (%)
4611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin161118
97.2%
Common4611
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r20861
12.9%
a16858
10.5%
i16328
10.1%
A15798
9.8%
e12734
7.9%
o12204
 
7.6%
c11026
 
6.8%
u9432
 
5.9%
E7593
 
4.7%
p7593
 
4.7%
Other values (9)30691
19.0%
Common
ValueCountFrequency (%)
4611
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII165729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r20861
12.6%
a16858
10.2%
i16328
9.9%
A15798
9.5%
e12734
 
7.7%
o12204
 
7.4%
c11026
 
6.7%
u9432
 
5.7%
E7593
 
4.6%
p7593
 
4.6%
Other values (10)35302
21.3%

away_team_continent
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Europe
7359 
Africa
6306 
Asia
4817 
North America
2703 
South America
2161 

Length

Max length13
Median length6
Mean length7.044646963
Min length4

Characters and Unicode

Total characters168515
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth America
2nd rowNorth America
3rd rowSouth America
4th rowAfrica
5th rowSouth America

Common Values

ValueCountFrequency (%)
Europe7359
30.8%
Africa6306
26.4%
Asia4817
20.1%
North America2703
 
11.3%
South America2161
 
9.0%
Oceania575
 
2.4%

Length

2022-10-09T12:05:50.703985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-09T12:05:50.797734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
europe7359
25.6%
africa6306
21.9%
america4864
16.9%
asia4817
16.7%
north2703
 
9.4%
south2161
 
7.5%
oceania575
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r21232
12.6%
a17137
10.2%
i16562
9.8%
A15987
9.5%
e12798
 
7.6%
o12223
 
7.3%
c11745
 
7.0%
u9520
 
5.6%
E7359
 
4.4%
p7359
 
4.4%
Other values (10)36593
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter134866
80.0%
Uppercase Letter28785
 
17.1%
Space Separator4864
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r21232
15.7%
a17137
12.7%
i16562
12.3%
e12798
9.5%
o12223
9.1%
c11745
8.7%
u9520
7.1%
p7359
 
5.5%
f6306
 
4.7%
t4864
 
3.6%
Other values (4)15120
11.2%
Uppercase Letter
ValueCountFrequency (%)
A15987
55.5%
E7359
25.6%
N2703
 
9.4%
S2161
 
7.5%
O575
 
2.0%
Space Separator
ValueCountFrequency (%)
4864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin163651
97.1%
Common4864
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r21232
13.0%
a17137
10.5%
i16562
10.1%
A15987
9.8%
e12798
7.8%
o12223
 
7.5%
c11745
 
7.2%
u9520
 
5.8%
E7359
 
4.5%
p7359
 
4.5%
Other values (9)31729
19.4%
Common
ValueCountFrequency (%)
4864
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII168515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r21232
12.6%
a17137
10.2%
i16562
9.8%
A15987
9.5%
e12798
 
7.6%
o12223
 
7.3%
c11745
 
7.0%
u9520
 
5.6%
E7359
 
4.4%
p7359
 
4.4%
Other values (10)36593
21.7%

home_team_fifa_rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct211
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.85468835
Minimum1
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:50.907475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q133
median71
Q3115
95-th percentile174
Maximum211
Range210
Interquartile range (IQR)82

Descriptive statistics

Standard deviation52.35522517
Coefficient of variation (CV)0.6724736337
Kurtosis-0.7546146622
Mean77.85468835
Median Absolute Deviation (MAD)41
Skewness0.4514227146
Sum1862362
Variance2741.069603
MonotonicityNot monotonic
2022-10-09T12:05:51.011196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22214
 
0.9%
3209
 
0.9%
29203
 
0.8%
5198
 
0.8%
11198
 
0.8%
10198
 
0.8%
1198
 
0.8%
4197
 
0.8%
34197
 
0.8%
12197
 
0.8%
Other values (201)21912
91.6%
ValueCountFrequency (%)
1198
0.8%
2187
0.8%
3209
0.9%
4197
0.8%
5198
0.8%
6183
0.8%
7178
0.7%
8196
0.8%
9177
0.7%
10198
0.8%
ValueCountFrequency (%)
2116
 
< 0.1%
21012
 
0.1%
20911
 
< 0.1%
2088
 
< 0.1%
20711
 
< 0.1%
20615
 
0.1%
20514
 
0.1%
20421
0.1%
20343
0.2%
20223
0.1%

away_team_fifa_rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct211
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.79737469
Minimum1
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:51.414088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q136
median73
Q3119
95-th percentile179
Maximum211
Range210
Interquartile range (IQR)83

Descriptive statistics

Standard deviation53.23290188
Coefficient of variation (CV)0.6588444499
Kurtosis-0.7663150753
Mean80.79737469
Median Absolute Deviation (MAD)41
Skewness0.4438615419
Sum1932754
Variance2833.741843
MonotonicityNot monotonic
2022-10-09T12:05:51.650971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1195
 
0.8%
29189
 
0.8%
14188
 
0.8%
38184
 
0.8%
18183
 
0.8%
4182
 
0.8%
55182
 
0.8%
36182
 
0.8%
37182
 
0.8%
27180
 
0.8%
Other values (201)22074
92.3%
ValueCountFrequency (%)
1195
0.8%
2180
0.8%
3151
0.6%
4182
0.8%
5174
0.7%
6168
0.7%
7175
0.7%
8157
0.7%
9136
0.6%
10157
0.7%
ValueCountFrequency (%)
2115
 
< 0.1%
21013
 
0.1%
20912
 
0.1%
2088
 
< 0.1%
20717
 
0.1%
20619
 
0.1%
20519
 
0.1%
20419
 
0.1%
20339
0.2%
20248
0.2%

home_team_total_fifa_points
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1686
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.4014882
Minimum0
Maximum2164
Zeros14290
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:51.798611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3547
95-th percentile1439
Maximum2164
Range2164
Interquartile range (IQR)547

Descriptive statistics

Standard deviation500.8257245
Coefficient of variation (CV)1.548619109
Kurtosis0.4078698977
Mean323.4014882
Median Absolute Deviation (MAD)0
Skewness1.348462631
Sum7736087
Variance250826.4064
MonotonicityNot monotonic
2022-10-09T12:05:51.913332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014290
59.7%
26027
 
0.1%
117422
 
0.1%
36920
 
0.1%
34019
 
0.1%
92419
 
0.1%
22819
 
0.1%
32319
 
0.1%
38918
 
0.1%
37418
 
0.1%
Other values (1676)9450
39.5%
ValueCountFrequency (%)
014290
59.7%
12
 
< 0.1%
22
 
< 0.1%
31
 
< 0.1%
46
 
< 0.1%
57
 
< 0.1%
62
 
< 0.1%
72
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
21642
< 0.1%
21602
< 0.1%
21242
< 0.1%
20362
< 0.1%
20171
< 0.1%
19981
< 0.1%
19552
< 0.1%
18322
< 0.1%
18281
< 0.1%
18272
< 0.1%

away_team_total_fifa_points
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1679
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.4535764
Minimum0
Maximum2164
Zeros14288
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:52.041985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3523
95-th percentile1416
Maximum2164
Range2164
Interquartile range (IQR)523

Descriptive statistics

Standard deviation490.9442731
Coefficient of variation (CV)1.556312275
Kurtosis0.4746270007
Mean315.4535764
Median Absolute Deviation (MAD)0
Skewness1.366652059
Sum7545965
Variance241026.2793
MonotonicityNot monotonic
2022-10-09T12:05:52.145279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014288
59.7%
55118
 
0.1%
37418
 
0.1%
29818
 
0.1%
31617
 
0.1%
25517
 
0.1%
33217
 
0.1%
32917
 
0.1%
137817
 
0.1%
33817
 
0.1%
Other values (1669)9477
39.6%
ValueCountFrequency (%)
014288
59.7%
14
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
411
 
< 0.1%
512
 
0.1%
63
 
< 0.1%
75
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
21641
 
< 0.1%
21242
< 0.1%
21041
 
< 0.1%
20994
< 0.1%
20871
 
< 0.1%
20411
 
< 0.1%
20362
< 0.1%
20141
 
< 0.1%
18324
< 0.1%
18281
 
< 0.1%

home_team_score
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.609213662
Minimum0
Maximum31
Zeros6273
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:52.243050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.630126712
Coefficient of variation (CV)1.01299582
Kurtosis12.8072405
Mean1.609213662
Median Absolute Deviation (MAD)1
Skewness2.210624947
Sum38494
Variance2.657313098
MonotonicityNot monotonic
2022-10-09T12:05:52.330815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
17229
30.2%
06273
26.2%
25263
22.0%
32613
 
10.9%
41330
 
5.6%
5583
 
2.4%
6292
 
1.2%
7142
 
0.6%
884
 
0.4%
943
 
0.2%
Other values (11)69
 
0.3%
ValueCountFrequency (%)
06273
26.2%
17229
30.2%
25263
22.0%
32613
 
10.9%
41330
 
5.6%
5583
 
2.4%
6292
 
1.2%
7142
 
0.6%
884
 
0.4%
943
 
0.2%
ValueCountFrequency (%)
311
 
< 0.1%
221
 
< 0.1%
192
 
< 0.1%
172
 
< 0.1%
163
 
< 0.1%
153
 
< 0.1%
146
< 0.1%
136
< 0.1%
129
< 0.1%
1112
0.1%

away_team_score
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.068266377
Minimum0
Maximum21
Zeros9558
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:52.418550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.263944313
Coefficient of variation (CV)1.183173355
Kurtosis10.92452874
Mean1.068266377
Median Absolute Deviation (MAD)1
Skewness2.187963747
Sum25554
Variance1.597555226
MonotonicityNot monotonic
2022-10-09T12:05:52.497369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
09558
40.0%
17759
32.4%
24013
16.8%
31551
 
6.5%
4594
 
2.5%
5214
 
0.9%
6107
 
0.4%
767
 
0.3%
827
 
0.1%
1011
 
< 0.1%
Other values (8)20
 
0.1%
ValueCountFrequency (%)
09558
40.0%
17759
32.4%
24013
16.8%
31551
 
6.5%
4594
 
2.5%
5214
 
0.9%
6107
 
0.4%
767
 
0.3%
827
 
0.1%
99
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
171
 
< 0.1%
152
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
113
 
< 0.1%
1011
< 0.1%
99
 
< 0.1%
827
0.1%

tournament
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct82
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Friendly
8558 
FIFA World Cup qualification
5528 
UEFA Euro qualification
1723 
African Cup of Nations qualification
1274 
AFC Asian Cup qualification
 
541
Other values (77)
6297 

Length

Max length42
Median length37
Mean length17.90719452
Min length7

Characters and Unicode

Total characters428358
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowFIFA World Cup qualification
2nd rowFriendly
3rd rowFIFA World Cup qualification
4th rowFriendly
5th rowFIFA World Cup qualification

Common Values

ValueCountFrequency (%)
Friendly8558
35.8%
FIFA World Cup qualification5528
23.1%
UEFA Euro qualification1723
 
7.2%
African Cup of Nations qualification1274
 
5.3%
AFC Asian Cup qualification541
 
2.3%
African Cup of Nations490
 
2.0%
FIFA World Cup432
 
1.8%
UEFA Nations League415
 
1.7%
COSAFA Cup309
 
1.3%
CECAFA Cup308
 
1.3%
Other values (72)4343
18.2%

Length

2022-10-09T12:05:52.599089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cup11350
18.5%
qualification9647
15.7%
friendly8558
14.0%
world5960
9.7%
fifa5960
9.7%
nations2763
 
4.5%
uefa2391
 
3.9%
african2053
 
3.4%
euro1976
 
3.2%
of1767
 
2.9%
Other values (89)8833
14.4%

Most occurring characters

ValueCountFrequency (%)
i46515
 
10.9%
37337
 
8.7%
a29604
 
6.9%
n26838
 
6.3%
F26533
 
6.2%
l25607
 
6.0%
o24292
 
5.7%
u24223
 
5.7%
r20428
 
4.8%
C16274
 
3.8%
Other values (44)150707
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter306531
71.6%
Uppercase Letter84387
 
19.7%
Space Separator37337
 
8.7%
Other Punctuation98
 
< 0.1%
Dash Punctuation5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i46515
15.2%
a29604
9.7%
n26838
8.8%
l25607
 
8.4%
o24292
 
7.9%
u24223
 
7.9%
r20428
 
6.7%
d15188
 
5.0%
f13897
 
4.5%
p13355
 
4.4%
Other values (18)66584
21.7%
Uppercase Letter
ValueCountFrequency (%)
F26533
31.4%
C16274
19.3%
A15106
17.9%
I6145
 
7.3%
W6081
 
7.2%
E4815
 
5.7%
N3205
 
3.8%
U2972
 
3.5%
O630
 
0.7%
G617
 
0.7%
Other values (12)2009
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
-4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
37337
100.0%
Other Punctuation
ValueCountFrequency (%)
'98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin390918
91.3%
Common37440
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i46515
 
11.9%
a29604
 
7.6%
n26838
 
6.9%
F26533
 
6.8%
l25607
 
6.6%
o24292
 
6.2%
u24223
 
6.2%
r20428
 
5.2%
C16274
 
4.2%
d15188
 
3.9%
Other values (40)135416
34.6%
Common
ValueCountFrequency (%)
37337
99.7%
'98
 
0.3%
-4
 
< 0.1%
1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII427968
99.9%
None389
 
0.1%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i46515
 
10.9%
37337
 
8.7%
a29604
 
6.9%
n26838
 
6.3%
F26533
 
6.2%
l25607
 
6.0%
o24292
 
5.7%
u24223
 
5.7%
r20428
 
4.8%
C16274
 
3.8%
Other values (40)150317
35.1%
None
ValueCountFrequency (%)
é304
78.1%
í77
 
19.8%
á8
 
2.1%
Punctuation
ValueCountFrequency (%)
1
100.0%

city
Categorical

HIGH CARDINALITY

Distinct1576
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Doha
 
397
Bangkok
 
215
Muscat
 
212
Kuwait City
 
202
Abu Dhabi
 
191
Other values (1571)
22704 

Length

Max length28
Median length24
Mean length7.723840977
Min length2

Characters and Unicode

Total characters184762
Distinct characters122
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique474 ?
Unique (%)2.0%

Sample

1st rowLa Paz
2nd rowMaceió
3rd rowQuito
4th rowConakry
5th rowAsunción

Common Values

ValueCountFrequency (%)
Doha397
 
1.7%
Bangkok215
 
0.9%
Muscat212
 
0.9%
Kuwait City202
 
0.8%
Abu Dhabi191
 
0.8%
London185
 
0.8%
Amman180
 
0.8%
Cairo164
 
0.7%
Dubai162
 
0.7%
Tehran161
 
0.7%
Other values (1566)21852
91.4%

Length

2022-10-09T12:05:52.706808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city527
 
1.9%
san435
 
1.5%
doha397
 
1.4%
port241
 
0.8%
bangkok215
 
0.8%
muscat212
 
0.7%
kuwait203
 
0.7%
abu191
 
0.7%
dhabi191
 
0.7%
london187
 
0.7%
Other values (1715)25660
90.2%

Most occurring characters

ValueCountFrequency (%)
a25514
 
13.8%
n12597
 
6.8%
o12492
 
6.8%
i12241
 
6.6%
e11368
 
6.2%
r10010
 
5.4%
u7653
 
4.1%
l7455
 
4.0%
t7391
 
4.0%
s7128
 
3.9%
Other values (112)70913
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter150864
81.7%
Uppercase Letter28402
 
15.4%
Space Separator4538
 
2.5%
Dash Punctuation530
 
0.3%
Other Punctuation417
 
0.2%
Initial Punctuation6
 
< 0.1%
Decimal Number5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a25514
16.9%
n12597
 
8.3%
o12492
 
8.3%
i12241
 
8.1%
e11368
 
7.5%
r10010
 
6.6%
u7653
 
5.1%
l7455
 
4.9%
t7391
 
4.9%
s7128
 
4.7%
Other values (66)37015
24.5%
Uppercase Letter
ValueCountFrequency (%)
S3091
10.9%
B2573
 
9.1%
A2400
 
8.5%
C2114
 
7.4%
M2033
 
7.2%
D1937
 
6.8%
L1929
 
6.8%
P1894
 
6.7%
K1600
 
5.6%
T1470
 
5.2%
Other values (29)7361
25.9%
Other Punctuation
ValueCountFrequency (%)
'251
60.2%
.162
38.8%
/4
 
1.0%
Space Separator
ValueCountFrequency (%)
4538
100.0%
Dash Punctuation
ValueCountFrequency (%)
-530
100.0%
Initial Punctuation
ValueCountFrequency (%)
6
100.0%
Decimal Number
ValueCountFrequency (%)
65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin179266
97.0%
Common5496
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a25514
 
14.2%
n12597
 
7.0%
o12492
 
7.0%
i12241
 
6.8%
e11368
 
6.3%
r10010
 
5.6%
u7653
 
4.3%
l7455
 
4.2%
t7391
 
4.1%
s7128
 
4.0%
Other values (105)65417
36.5%
Common
ValueCountFrequency (%)
4538
82.6%
-530
 
9.6%
'251
 
4.6%
.162
 
2.9%
6
 
0.1%
65
 
0.1%
/4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII182789
98.9%
None1954
 
1.1%
Latin Ext Additional7
 
< 0.1%
IPA Ext6
 
< 0.1%
Punctuation6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a25514
 
14.0%
n12597
 
6.9%
o12492
 
6.8%
i12241
 
6.7%
e11368
 
6.2%
r10010
 
5.5%
u7653
 
4.2%
l7455
 
4.1%
t7391
 
4.0%
s7128
 
3.9%
Other values (48)68940
37.7%
None
ValueCountFrequency (%)
é522
26.7%
ó297
15.2%
í185
 
9.5%
è104
 
5.3%
ș97
 
5.0%
ă85
 
4.4%
á72
 
3.7%
ã63
 
3.2%
à59
 
3.0%
ò57
 
2.9%
Other values (47)413
21.1%
IPA Ext
ValueCountFrequency (%)
ə6
100.0%
Punctuation
ValueCountFrequency (%)
6
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

country
Categorical

HIGH CARDINALITY

Distinct217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
USA
 
1003
South Africa
 
505
United Arab Emirates
 
462
Qatar
 
461
France
 
445
Other values (212)
21045 

Length

Max length30
Median length22
Mean length8.085238911
Min length3

Characters and Unicode

Total characters193407
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBolivia
2nd rowBrazil
3rd rowEcuador
4th rowGuinea
5th rowParaguay

Common Values

ValueCountFrequency (%)
USA1003
 
4.2%
South Africa505
 
2.1%
United Arab Emirates462
 
1.9%
Qatar461
 
1.9%
France445
 
1.9%
Germany285
 
1.2%
Saudi Arabia280
 
1.2%
Thailand280
 
1.2%
Japan277
 
1.2%
England277
 
1.2%
Other values (207)19646
82.1%

Length

2022-10-09T12:05:52.821502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa1003
 
3.4%
republic658
 
2.2%
south513
 
1.7%
and509
 
1.7%
africa505
 
1.7%
united462
 
1.5%
arab462
 
1.5%
emirates462
 
1.5%
qatar461
 
1.5%
france445
 
1.5%
Other values (244)24377
81.6%

Most occurring characters

ValueCountFrequency (%)
a29504
15.3%
i15481
 
8.0%
n14874
 
7.7%
e12133
 
6.3%
r11936
 
6.2%
o9474
 
4.9%
t7232
 
3.7%
l7079
 
3.7%
u6744
 
3.5%
d6303
 
3.3%
Other values (50)72647
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter155498
80.4%
Uppercase Letter31703
 
16.4%
Space Separator5936
 
3.1%
Other Punctuation243
 
0.1%
Dash Punctuation27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a29504
19.0%
i15481
10.0%
n14874
9.6%
e12133
 
7.8%
r11936
 
7.7%
o9474
 
6.1%
t7232
 
4.7%
l7079
 
4.6%
u6744
 
4.3%
d6303
 
4.1%
Other values (21)34738
22.3%
Uppercase Letter
ValueCountFrequency (%)
S3941
12.4%
A3577
 
11.3%
U2006
 
6.3%
C1924
 
6.1%
M1879
 
5.9%
R1800
 
5.7%
B1795
 
5.7%
E1723
 
5.4%
I1701
 
5.4%
T1556
 
4.9%
Other values (15)9801
30.9%
Other Punctuation
ValueCountFrequency (%)
.151
62.1%
'92
37.9%
Space Separator
ValueCountFrequency (%)
5936
100.0%
Dash Punctuation
ValueCountFrequency (%)
-27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin187201
96.8%
Common6206
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a29504
15.8%
i15481
 
8.3%
n14874
 
7.9%
e12133
 
6.5%
r11936
 
6.4%
o9474
 
5.1%
t7232
 
3.9%
l7079
 
3.8%
u6744
 
3.6%
d6303
 
3.4%
Other values (46)66441
35.5%
Common
ValueCountFrequency (%)
5936
95.6%
.151
 
2.4%
'92
 
1.5%
-27
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII193237
99.9%
None170
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a29504
15.3%
i15481
 
8.0%
n14874
 
7.7%
e12133
 
6.3%
r11936
 
6.2%
o9474
 
4.9%
t7232
 
3.7%
l7079
 
3.7%
u6744
 
3.5%
d6303
 
3.3%
Other values (45)72477
37.5%
None
ValueCountFrequency (%)
ô92
54.1%
ç31
 
18.2%
é17
 
10.0%
ã15
 
8.8%
í15
 
8.8%

neutral_location
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
False
17947 
True
5974 
ValueCountFrequency (%)
False17947
75.0%
True5974
 
25.0%
2022-10-09T12:05:52.910233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shoot_out
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
False
23589 
True
 
332
ValueCountFrequency (%)
False23589
98.6%
True332
 
1.4%
2022-10-09T12:05:52.979049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

home_team_result
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
Win
11761 
Lose
6771 
Draw
5389 

Length

Max length4
Median length4
Mean length3.508339952
Min length3

Characters and Unicode

Total characters83923
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWin
2nd rowDraw
3rd rowWin
4th rowWin
5th rowLose

Common Values

ValueCountFrequency (%)
Win11761
49.2%
Lose6771
28.3%
Draw5389
22.5%

Length

2022-10-09T12:05:53.057410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-09T12:05:53.141221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
win11761
49.2%
lose6771
28.3%
draw5389
22.5%

Most occurring characters

ValueCountFrequency (%)
W11761
14.0%
i11761
14.0%
n11761
14.0%
L6771
8.1%
o6771
8.1%
s6771
8.1%
e6771
8.1%
D5389
6.4%
r5389
6.4%
a5389
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60002
71.5%
Uppercase Letter23921
 
28.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i11761
19.6%
n11761
19.6%
o6771
11.3%
s6771
11.3%
e6771
11.3%
r5389
9.0%
a5389
9.0%
w5389
9.0%
Uppercase Letter
ValueCountFrequency (%)
W11761
49.2%
L6771
28.3%
D5389
22.5%

Most occurring scripts

ValueCountFrequency (%)
Latin83923
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W11761
14.0%
i11761
14.0%
n11761
14.0%
L6771
8.1%
o6771
8.1%
s6771
8.1%
e6771
8.1%
D5389
6.4%
r5389
6.4%
a5389
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII83923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W11761
14.0%
i11761
14.0%
n11761
14.0%
L6771
8.1%
o6771
8.1%
s6771
8.1%
e6771
8.1%
D5389
6.4%
r5389
6.4%
a5389
6.4%

home_team_goalkeeper_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct50
Distinct (%)0.6%
Missing15542
Missing (%)65.0%
Infinite0
Infinite (%)0.0%
Mean74.96383817
Minimum47
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:53.227000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile61
Q170
median75
Q381
95-th percentile88
Maximum97
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.212242192
Coefficient of variation (CV)0.1095493826
Kurtosis0.1345376971
Mean74.96383817
Median Absolute Deviation (MAD)6
Skewness-0.2847013488
Sum628122
Variance67.44092181
MonotonicityNot monotonic
2022-10-09T12:05:53.339659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73474
 
2.0%
74441
 
1.8%
75437
 
1.8%
76420
 
1.8%
72403
 
1.7%
77374
 
1.6%
79347
 
1.5%
81344
 
1.4%
80325
 
1.4%
82324
 
1.4%
Other values (40)4490
 
18.8%
(Missing)15542
65.0%
ValueCountFrequency (%)
476
 
< 0.1%
487
 
< 0.1%
499
 
< 0.1%
5012
 
0.1%
5117
0.1%
5230
0.1%
5324
0.1%
5420
0.1%
5542
0.2%
5632
0.1%
ValueCountFrequency (%)
976
 
< 0.1%
9519
 
0.1%
9426
 
0.1%
9331
 
0.1%
9222
 
0.1%
9151
 
0.2%
90113
0.5%
89120
0.5%
88111
0.5%
87145
0.6%

away_team_goalkeeper_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct50
Distinct (%)0.6%
Missing15826
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean74.21247684
Minimum47
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:53.443884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile61
Q169
median74
Q380
95-th percentile87
Maximum97
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.225919096
Coefficient of variation (CV)0.110842805
Kurtosis0.1394057478
Mean74.21247684
Median Absolute Deviation (MAD)6
Skewness-0.2838054963
Sum600750
Variance67.66574498
MonotonicityNot monotonic
2022-10-09T12:05:53.561125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75473
 
2.0%
73466
 
1.9%
72424
 
1.8%
74404
 
1.7%
76365
 
1.5%
77343
 
1.4%
69340
 
1.4%
81313
 
1.3%
78311
 
1.3%
79310
 
1.3%
Other values (40)4346
 
18.2%
(Missing)15826
66.2%
ValueCountFrequency (%)
476
 
< 0.1%
4810
 
< 0.1%
4910
 
< 0.1%
5021
0.1%
5121
0.1%
5230
0.1%
5323
0.1%
5424
0.1%
5547
0.2%
5639
0.2%
ValueCountFrequency (%)
975
 
< 0.1%
9512
 
0.1%
9414
 
0.1%
9324
 
0.1%
9216
 
0.1%
9143
 
0.2%
9087
0.4%
8991
0.4%
8897
0.4%
87122
0.5%

home_team_mean_defense_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct127
Distinct (%)1.6%
Missing16134
Missing (%)67.4%
Infinite0
Infinite (%)0.0%
Mean74.903249
Minimum52.8
Maximum91.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:53.671829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52.8
5-th percentile65
Q171
median75.2
Q378.8
95-th percentile85
Maximum91.8
Range39
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation6.003114482
Coefficient of variation (CV)0.08014491443
Kurtosis0.02149570954
Mean74.903249
Median Absolute Deviation (MAD)3.8
Skewness-0.1065522626
Sum583271.6
Variance36.03738348
MonotonicityNot monotonic
2022-10-09T12:05:53.784548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.5194
 
0.8%
77182
 
0.8%
76178
 
0.7%
76.5177
 
0.7%
75.2159
 
0.7%
78.2150
 
0.6%
71.5143
 
0.6%
77.8142
 
0.6%
70.8141
 
0.6%
74.2140
 
0.6%
Other values (117)6181
 
25.8%
(Missing)16134
67.4%
ValueCountFrequency (%)
52.86
 
< 0.1%
56.511
< 0.1%
57.51
 
< 0.1%
57.88
< 0.1%
58.23
 
< 0.1%
58.516
0.1%
58.83
 
< 0.1%
598
< 0.1%
59.24
 
< 0.1%
59.510
< 0.1%
ValueCountFrequency (%)
91.86
 
< 0.1%
90.54
 
< 0.1%
90.210
 
< 0.1%
89.511
 
< 0.1%
897
 
< 0.1%
8821
0.1%
87.815
 
0.1%
87.543
0.2%
87.210
 
< 0.1%
8722
0.1%

home_team_mean_offense_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct103
Distinct (%)1.2%
Missing15411
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean75.81874266
Minimum53.3
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:53.899221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53.3
5-th percentile66
Q171.7
median75.7
Q380
95-th percentile86.7
Maximum93
Range39.7
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation6.26841591
Coefficient of variation (CV)0.08267633689
Kurtosis-0.05510707391
Mean75.81874266
Median Absolute Deviation (MAD)4.3
Skewness0.01423465344
Sum645217.5
Variance39.29303802
MonotonicityNot monotonic
2022-10-09T12:05:54.004973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.3229
 
1.0%
77.7223
 
0.9%
72.7218
 
0.9%
76.7218
 
0.9%
71.3218
 
0.9%
72.3215
 
0.9%
74.7197
 
0.8%
73.3194
 
0.8%
73.7184
 
0.8%
75.7174
 
0.7%
Other values (93)6440
26.9%
(Missing)15411
64.4%
ValueCountFrequency (%)
53.34
 
< 0.1%
553
 
< 0.1%
57.76
 
< 0.1%
587
 
< 0.1%
58.34
 
< 0.1%
5912
0.1%
59.33
 
< 0.1%
59.723
0.1%
6014
0.1%
60.318
0.1%
ValueCountFrequency (%)
9313
 
0.1%
92.77
 
< 0.1%
92.313
 
0.1%
9119
0.1%
90.76
 
< 0.1%
90.313
 
0.1%
9025
0.1%
89.334
0.1%
8912
 
0.1%
88.737
0.2%

home_team_mean_midfield_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct134
Distinct (%)1.6%
Missing15759
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean75.88929184
Minimum54.2
Maximum93.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:54.316138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum54.2
5-th percentile65
Q172.5
median76.2
Q379.5
95-th percentile86
Maximum93.2
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.053109555
Coefficient of variation (CV)0.07976236711
Kurtosis0.2554212993
Mean75.88929184
Median Absolute Deviation (MAD)3.4
Skewness-0.2912718461
Sum619408.4
Variance36.64013529
MonotonicityNot monotonic
2022-10-09T12:05:54.426355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.2243
 
1.0%
76.8207
 
0.9%
75192
 
0.8%
74.8189
 
0.8%
78.2188
 
0.8%
78.5182
 
0.8%
75.2169
 
0.7%
78164
 
0.7%
79.2158
 
0.7%
77.2157
 
0.7%
Other values (124)6313
26.4%
(Missing)15759
65.9%
ValueCountFrequency (%)
54.22
 
< 0.1%
55.57
< 0.1%
561
 
< 0.1%
56.82
 
< 0.1%
57.27
< 0.1%
57.512
0.1%
57.88
< 0.1%
588
< 0.1%
58.23
 
< 0.1%
58.51
 
< 0.1%
ValueCountFrequency (%)
93.27
 
< 0.1%
927
 
< 0.1%
91.24
 
< 0.1%
89.87
 
< 0.1%
89.514
0.1%
89.29
 
< 0.1%
8919
0.1%
88.84
 
< 0.1%
88.524
0.1%
88.217
0.1%

away_team_mean_defense_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct127
Distinct (%)1.7%
Missing16357
Missing (%)68.4%
Infinite0
Infinite (%)0.0%
Mean74.42437864
Minimum52.8
Maximum91.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:54.560058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52.8
5-th percentile64.8
Q170.5
median74.5
Q378.2
95-th percentile84.8
Maximum91.8
Range39
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation5.937425305
Coefficient of variation (CV)0.07977796273
Kurtosis0.02034776311
Mean74.42437864
Median Absolute Deviation (MAD)3.7
Skewness-0.04461795013
Sum562946
Variance35.25301925
MonotonicityNot monotonic
2022-10-09T12:05:54.666777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.5175
 
0.7%
77167
 
0.7%
76162
 
0.7%
74.5157
 
0.7%
75.2157
 
0.7%
76.5152
 
0.6%
70.8151
 
0.6%
74.2148
 
0.6%
71.5138
 
0.6%
78.2136
 
0.6%
Other values (117)6021
 
25.2%
(Missing)16357
68.4%
ValueCountFrequency (%)
52.87
< 0.1%
56.58
< 0.1%
57.54
 
< 0.1%
57.85
 
< 0.1%
58.24
 
< 0.1%
58.512
0.1%
58.86
< 0.1%
5910
< 0.1%
59.21
 
< 0.1%
59.513
0.1%
ValueCountFrequency (%)
91.85
 
< 0.1%
90.57
 
< 0.1%
90.27
 
< 0.1%
89.53
 
< 0.1%
8910
 
< 0.1%
8817
0.1%
87.816
0.1%
87.530
0.1%
87.210
 
< 0.1%
8721
0.1%

away_team_mean_offense_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct103
Distinct (%)1.2%
Missing15609
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean75.42001925
Minimum53.3
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:54.791442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53.3
5-th percentile65.7
Q171.3
median75.3
Q379.7
95-th percentile86
Maximum93
Range39.7
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation6.201905739
Coefficient of variation (CV)0.08223155869
Kurtosis-0.05994827244
Mean75.42001925
Median Absolute Deviation (MAD)4
Skewness-0.006600849852
Sum626891.2
Variance38.4636348
MonotonicityNot monotonic
2022-10-09T12:05:54.895683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.3238
 
1.0%
71.3234
 
1.0%
72.7213
 
0.9%
76.7206
 
0.9%
76.3203
 
0.8%
77.7200
 
0.8%
73192
 
0.8%
70.7184
 
0.8%
75.3180
 
0.8%
74.7178
 
0.7%
Other values (93)6284
26.3%
(Missing)15609
65.3%
ValueCountFrequency (%)
53.34
 
< 0.1%
554
 
< 0.1%
57.77
 
< 0.1%
588
 
< 0.1%
58.33
 
< 0.1%
5912
 
0.1%
59.35
 
< 0.1%
59.730
0.1%
606
 
< 0.1%
60.320
0.1%
ValueCountFrequency (%)
938
 
< 0.1%
92.75
 
< 0.1%
92.315
0.1%
9113
0.1%
90.76
 
< 0.1%
90.38
 
< 0.1%
9013
0.1%
89.324
0.1%
8915
0.1%
88.726
0.1%

away_team_mean_midfield_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct134
Distinct (%)1.7%
Missing15942
Missing (%)66.6%
Infinite0
Infinite (%)0.0%
Mean75.25914275
Minimum54.2
Maximum93.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.0 KiB
2022-10-09T12:05:55.009412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum54.2
5-th percentile63.8
Q171.8
median75.5
Q379
95-th percentile85.5
Maximum93.2
Range39
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation6.124573345
Coefficient of variation (CV)0.0813797915
Kurtosis0.188041849
Mean75.25914275
Median Absolute Deviation (MAD)3.7
Skewness-0.2751545753
Sum600492.7
Variance37.51039866
MonotonicityNot monotonic
2022-10-09T12:05:55.123075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.2208
 
0.9%
75196
 
0.8%
74.8185
 
0.8%
78169
 
0.7%
76.8169
 
0.7%
78.5168
 
0.7%
77.2166
 
0.7%
78.2165
 
0.7%
74.5156
 
0.7%
75.2155
 
0.6%
Other values (124)6242
 
26.1%
(Missing)15942
66.6%
ValueCountFrequency (%)
54.210
< 0.1%
55.56
< 0.1%
562
 
< 0.1%
56.83
 
< 0.1%
57.28
< 0.1%
57.57
< 0.1%
57.86
< 0.1%
586
< 0.1%
58.26
< 0.1%
58.51
 
< 0.1%
ValueCountFrequency (%)
93.23
 
< 0.1%
925
 
< 0.1%
91.27
 
< 0.1%
89.810
 
< 0.1%
89.58
 
< 0.1%
89.25
 
< 0.1%
8915
0.1%
88.82
 
< 0.1%
88.525
0.1%
88.28
 
< 0.1%

Interactions

2022-10-09T12:05:47.016346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:26.961479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.548512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.989749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.494322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.904037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.355747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.890191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.258783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.785125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.559592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.120567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.816178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.404404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.110936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.084123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.641294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.079972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.611997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.999762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.449986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.992918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.350539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.892860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.682263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.226250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.976781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.500149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.216724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.185822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.733055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.173295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.714137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.093507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.552997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.083246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.450275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.998576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.785986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.329014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.084460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.609895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.311467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.286549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.828792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.265081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.806881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.189288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.799022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.184996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.542994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.102276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.890707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.425748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.190215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.709271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.409634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.394262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.922539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.358822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.897614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.283543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.887824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.276762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.633753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.217969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.988446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.526448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.301915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.819942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.517920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.527904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.021248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.455542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.997348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.380819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.981647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.377492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.722900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.344628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.099149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.645131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.430573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.923665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.625206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.652572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.115992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.547295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.092098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.473107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.065959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.463766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.816367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.476276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.212883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.921795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.556105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.020440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.724939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.780230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.219716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.639083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.190865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.570881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.154182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.556058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.907093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.601954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.315609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.019877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.663386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.118178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.819717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:27.885946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.335431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.886423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.282586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.659610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.245941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.650414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.997485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.703250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.421325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.115621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.760127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.211927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:47.919455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.010612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.445113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:30.985122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.392331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.762363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.348640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.756099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.094230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.812957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.532076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.226754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.861886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.319640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:48.025136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.118622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.567784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.084938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.492024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.866057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.449400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.855861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.191511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:39.923661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.666748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.339454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:44.967573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.420401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:48.129855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.228366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.669547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.187088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.599769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:33.978782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.560073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:36.956597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.295720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.045029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.794406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.456143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.074287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.528084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:48.239598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.332086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.774291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.290810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.697087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.094473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.659857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.057317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.399618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.283199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:41.904112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.571832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.182996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.636829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:48.346278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:28.436806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:29.880011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:31.388581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:32.798296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:34.212132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:35.768517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:37.158023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:38.684426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:40.434927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:42.009829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:43.688544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:45.292739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-09T12:05:46.911089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-09T12:05:55.229789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-09T12:05:55.454188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-09T12:05:55.681580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-09T12:05:55.903061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-09T12:05:56.039692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-09T12:05:48.563728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-09T12:05:49.340265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-09T12:05:49.721244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-09T12:05:49.942656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datehome_teamaway_teamhome_team_continentaway_team_continenthome_team_fifa_rankaway_team_fifa_rankhome_team_total_fifa_pointsaway_team_total_fifa_pointshome_team_scoreaway_team_scoretournamentcitycountryneutral_locationshoot_outhome_team_resulthome_team_goalkeeper_scoreaway_team_goalkeeper_scorehome_team_mean_defense_scorehome_team_mean_offense_scorehome_team_mean_midfield_scoreaway_team_mean_defense_scoreaway_team_mean_offense_scoreaway_team_mean_midfield_score
01993-08-08BoliviaUruguaySouth AmericaSouth America59220031FIFA World Cup qualificationLa PazBoliviaFalseNoWinNaNNaNNaNNaNNaNNaNNaNNaN
11993-08-08BrazilMexicoSouth AmericaNorth America8140011FriendlyMaceióBrazilFalseNoDrawNaNNaNNaNNaNNaNNaNNaNNaN
21993-08-08EcuadorVenezuelaSouth AmericaSouth America35940050FIFA World Cup qualificationQuitoEcuadorFalseNoWinNaNNaNNaNNaNNaNNaNNaNNaN
31993-08-08GuineaSierra LeoneAfricaAfrica65860010FriendlyConakryGuineaFalseNoWinNaNNaNNaNNaNNaNNaNNaNNaN
41993-08-08ParaguayArgentinaSouth AmericaSouth America6750013FIFA World Cup qualificationAsunciónParaguayFalseNoLoseNaNNaNNaNNaNNaNNaNNaNNaN
51993-08-08PeruColombiaSouth AmericaSouth America70190001FIFA World Cup qualificationLimaPeruFalseNoLoseNaNNaNNaNNaNNaNNaNNaNNaN
61993-08-08ZimbabweEswatiniAfricaAfrica501020020FriendlyHarareZimbabweFalseNoWinNaNNaNNaNNaNNaNNaNNaNNaN
71993-08-09GuineaSierra LeoneAfricaAfrica65860040FriendlyConakryGuineaFalseNoWinNaNNaNNaNNaNNaNNaNNaNNaN
81993-08-11Faroe IslandsNorwayEuropeEurope11190007FriendlyToftirFaroe IslandsFalseNoLoseNaNNaNNaNNaNNaNNaNNaNNaN
91993-08-11SwedenSwitzerlandEuropeEurope430012FriendlyBoråsSwedenFalseNoLoseNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

datehome_teamaway_teamhome_team_continentaway_team_continenthome_team_fifa_rankaway_team_fifa_rankhome_team_total_fifa_pointsaway_team_total_fifa_pointshome_team_scoreaway_team_scoretournamentcitycountryneutral_locationshoot_outhome_team_resulthome_team_goalkeeper_scoreaway_team_goalkeeper_scorehome_team_mean_defense_scorehome_team_mean_offense_scorehome_team_mean_midfield_scoreaway_team_mean_defense_scoreaway_team_mean_offense_scoreaway_team_mean_midfield_score
239112022-06-14UkraineRepublic of IrelandEuropeEurope27471535144911UEFA Nations LeagueŁódźPolandTrueNoDraw75.075.074.878.780.076.572.773.8
239122022-06-14Bosnia and HerzegovinaFinlandEuropeEurope59571388140632UEFA Nations LeagueZenicaBosnia and HerzegovinaFalseNoWin76.083.074.277.078.070.072.373.5
239132022-06-14RomaniaMontenegroEuropeEurope48701446134203UEFA Nations LeagueBucharestRomaniaFalseNoLose77.065.073.573.775.076.274.768.2
239142022-06-14LuxembourgFaroe IslandsEuropeEurope941241229113722UEFA Nations LeagueLuxembourgLuxembourgFalseNoDraw69.0NaN68.5NaN69.8NaNNaNNaN
239152022-06-14TurkeyLithuaniaEuropeEurope431381461109220UEFA Nations LeagueİzmirTurkeyFalseNoWin79.071.078.276.778.2NaNNaNNaN
239162022-06-14MoldovaAndorraEuropeEurope180153932104021UEFA Nations LeagueChișinăuMoldovaFalseNoWin65.0NaNNaNNaNNaNNaNNaNNaN
239172022-06-14LiechtensteinLatviaEuropeEurope192135895110502UEFA Nations LeagueVaduzLiechtensteinFalseNoLoseNaN65.0NaNNaNNaNNaNNaNNaN
239182022-06-14ChileGhanaSouth AmericaAfrica28601526138700Kirin CupSuitaJapanTrueYesLose79.074.075.576.778.275.576.078.2
239192022-06-14JapanTunisiaAsiaAfrica23351553149903Kirin CupSuitaJapanFalseNoLose73.0NaN75.275.077.570.872.374.0
239202022-06-14Korea RepublicEgyptAsiaAfrica29321519150041FriendlySeoulKorea RepublicFalseNoWin75.0NaN73.080.073.8NaN79.370.8